pullit!

pullit together with storethat are the two workhorses of the finRes suite. Install the development version with devtools::install_github("bautheac/pullit").

Using carefully selected Bloomberg datafields from BBGsymbols in tandem with Armstrong, Eddelbuettel, and Laing (2018)’s Bloomberg interface pullit provides the R user with easy access to Bloomberg financial data for a collection of financial instruments that, at the time of writing, includes equity and equity-like securities, funds with the category encompassing any money-managing entity, as well as futures series & term structure individual contracts.

Bloomberg

An active Bloomberg connection is required to retrieve Bloomberg financial data using pullit.

library(pullit); library(lubridate)

end <- Sys.Date() - years(1L); start <- end - years(2L)

equity

Equity data comes in three major categories in pullit, market, book and info. For a given corporation market data records stock market activity while book data records business activity in the form of financial statements commonly refered to as ‘books’ and info refers to qualitative information. See the ‘fields’ dataset in BBGsymbols for a detailed list of the Bloomberg fields available.

market

Retrieve market data from Bloomberg for Archer-Daniels-Midland (ADM US Equity), Kraft-Heinz (KHC US Equity) and XPO Logistics (XPO US Equity) with:

equity_tickers <- c("ADM US Equity", "KHC US Equity", "XPO US Equity")
equity_market <- BBG_equity_market(equity_tickers, start, end, verbose = FALSE)

books

For financial statements data BBGsymbols replicates the Bloomberg ‘financial analysis’ monitor (FA ). At the time of writing, this includes balance sheet, cash flow statement, income statement as well as ‘key stats’ that gathers broad summary figures and ‘ratios’ that includes popular financial ratios.

Retrieve the corresponding data for the abovementioned corporations with:

balance sheet

equity_BS <- BBG_equity_book(book = "balance sheet", equity_tickers, start, end, verbose = FALSE)

cash flow statement

equity_CF <- BBG_equity_book(book = "cash flow statement", equity_tickers, start, end, verbose = FALSE)

income statement

equity_IS <- BBG_equity_book(book = "income statement", equity_tickers, start, end, verbose = FALSE)

key stats

equity_KS <- BBG_equity_book(book = "key stats", equity_tickers, start, end, verbose = FALSE)

ratios

equity_R <- BBG_equity_book(book = "ratios", equity_tickers, start, end, verbose = FALSE)

info

‘info’ encompasses a range of contemporaneous qualitative information including, but not limited to, firm’s name, security type, exchange where the security trades, country of incorporation, etc.

Retrieve the corresponding data for the abovementioned corporations with:

equity_info <- BBG_equity_info(equity_tickers, verbose = FALSE)

fund

Fund data only comes in two categories, market and info. For a given fund market data records stock market activity while info data records contemporaneous qualitative information that includes a wide range of fund characteristics. Market historical data as well as contemporaneous qualitative data not only allow for an indepth assessment of the fund performance but potentially also the mapping of the performance to fund characteristics. See the ‘fields’ dataset in BBGsymbols for a detailed list of the Bloomberg datafields available.

market

Retrieve market data from Bloomberg for the SPDR S&P 500 ETF Trust (SPY US Equity), the SPDR Gold Shares investment fund (GLD US Equity) and the iShares MSCI Emerging Markets ETF (EEM US Equity) with:

fund_tickers <- c("SPY US Equity", "GLD US Equity", "EEM US Equity")

fund_market <- BBG_fund_market(fund_tickers, start, end, verbose = FALSE)

info

Retrieve the corresponding qualitative data for the abovementioned funds with:

fund_info <- BBG_fund_info(fund_tickers, verbose = FALSE)

futures

Futures data comes in three major categories in pullit, market, CFTC and info. For a given futures series market data records futures market price activity while CFTC data records market positions. ‘info’ on the other hand records contemporaneous qualitative information for the corresponding futures series, including but not limited to series name, term structure length, contract size, contract unit, etc. See the ‘fields’ dataset in BBGsymbols for a detailed list of the Bloomberg datafields available.

market

pullit helps retrieving both term structure as well as aggregated market data for futures. Term structure data records market activity for individual futures term structure contracts while aggregated data records market activity measures that are aggregated over the whole term structure for a particular futures series.

term structure

Retrieve market data from Bloomberg for the five first term structure contracts on the Corn - #2-yellow (C A Comdty), Eurodollar (EDA Comdty) and S&P500 - e-mini (ESA Index) futures series where the futures chains are constructed by rolling on liquidity (with active contract: “A”) with no roll adjustment (none: “N”) using:

futures_tickers <- c("C A Comdty", "EDA Comdty", "ESA Index")

futures_TS <- BBG_futures_market(type = "term structure", active_contract_tickers = futures_tickers, 
                                 start, end, TS_positions = 1L:5L, roll_type = "A", roll_days = 0L, 
                                 roll_months = 0L, roll_adjustment = "N", verbose = FALSE)

aggregate

Retrieve the corresponding aggregated futures market data with:

futures_agg <- BBG_futures_market(type = "aggregate", active_contract_tickers = futures_tickers,
                                  start, end, verbose = FALSE)

CFTC

pullit helps retrieving CFTC futures market position data from Bloomberg. The Commodity Futures Trading Commission (CFTC) publishes the Commitments of Traders (COT) reports to help the public understand market dynamics. Specifically, the COT reports provide a breakdown of each Tuesday’s open interest for futures and options on futures markets in which 20 or more traders hold positions equal to or above the reporting levels established by the CFTC. See the ‘fields’ and ’’ datasets in BBGsymbols for a details.

Retrieve the corresponding futures market position data with:

futures_CFTC <- BBG_futures_CFTC(active_contract_tickers = futures_tickers, start, end, verbose = FALSE)

info

‘info’ encompasses a range of contemporaneous qualitative information on the underlying futures series including, but not limited to, name for the series’ underlying, trading exchange, term structure length, contract size, etc.

Retrieve the corresponding data for the abovementioned futures series with:

futures_info <- BBG_futures_info(futures_tickers, verbose = FALSE)

accessors

The functions above return objects that not only carry the retrieved financial data but also complementary information that can be accessed using the @ operator or more formaly using bespoke accessor methods. Historical data functions for example return objects that carry a ‘tickers’ dataframe that indicates the tickers for which some data have been found, a ‘fields’ dataframe that indicates the data fields for which data has been found, a ‘data’ dataframe that hosts the retrieved data as well as a character vector hosting the original call to the function. A get_periods() method complements the accessor methods by indicating the start and end dates between which data have been found for each ticker and data field:

equity_market
#> S4 object of class EquityMarket 
#> 
#> Slots inlude
#>    tickers: access with get_tickers()
#>    fields: access with get_fields()
#>    data: access with get_data()
#>    call: access with get_call()
#>  
#> See also: get_periods()

Access each slot using the appropriate accessor:

get_tickers(equity_market)
#>           ticker
#> 1: ADM US Equity
#> 2: KHC US Equity
#> 3: XPO US Equity

get_fields(equity_market)
#>            ticker instrument   book      symbol
#>  1: ADM US Equity     equity market CUR_MKT_CAP
#>  2: ADM US Equity     equity market  EQY_SH_OUT
#>  3: ADM US Equity     equity market      PX_ASK
#>  4: ADM US Equity     equity market      PX_BID
#>  5: ADM US Equity     equity market     PX_HIGH
#>  6: ADM US Equity     equity market     PX_LAST
#>  7: ADM US Equity     equity market      PX_LOW
#>  8: ADM US Equity     equity market      PX_MID
#>  9: ADM US Equity     equity market     PX_OPEN
#> 10: ADM US Equity     equity market   PX_VOLUME
#> 11: KHC US Equity     equity market CUR_MKT_CAP
#> 12: KHC US Equity     equity market  EQY_SH_OUT
#> 13: KHC US Equity     equity market      PX_ASK
#> 14: KHC US Equity     equity market      PX_BID
#> 15: KHC US Equity     equity market     PX_HIGH
#> 16: KHC US Equity     equity market     PX_LAST
#> 17: KHC US Equity     equity market      PX_LOW
#> 18: KHC US Equity     equity market      PX_MID
#> 19: KHC US Equity     equity market     PX_OPEN
#> 20: KHC US Equity     equity market   PX_VOLUME
#> 21: XPO US Equity     equity market CUR_MKT_CAP
#> 22: XPO US Equity     equity market  EQY_SH_OUT
#> 23: XPO US Equity     equity market      PX_ASK
#> 24: XPO US Equity     equity market      PX_BID
#> 25: XPO US Equity     equity market     PX_HIGH
#> 26: XPO US Equity     equity market     PX_LAST
#> 27: XPO US Equity     equity market      PX_LOW
#> 28: XPO US Equity     equity market      PX_MID
#> 29: XPO US Equity     equity market     PX_OPEN
#> 30: XPO US Equity     equity market   PX_VOLUME
#>            ticker instrument   book      symbol

get_data(equity_market)
#>              ticker       field       date      value
#>    1: ADM US Equity CUR_MKT_CAP 2016-10-31 25347.4219
#>    2: ADM US Equity CUR_MKT_CAP 2016-11-01  26899.934
#>    3: ADM US Equity CUR_MKT_CAP 2016-11-02 27193.3878
#>    4: ADM US Equity CUR_MKT_CAP 2016-11-03 27291.2058
#>    5: ADM US Equity CUR_MKT_CAP 2016-11-04  26997.752
#>   ---                                                
#> 7976: XPO US Equity   PX_VOLUME 2017-11-13   887972.0
#> 7977: XPO US Equity   PX_VOLUME 2017-11-14  1221359.0
#> 7978: XPO US Equity   PX_VOLUME 2017-11-15  2163577.0
#> 7979: XPO US Equity   PX_VOLUME 2017-11-16  1205978.0
#> 7980: XPO US Equity   PX_VOLUME 2017-11-17  1201417.0

get_call(equity_market)
#> storethat_equity_market(tickers = equity_tickers, start = start, 
#>     end = end, file = "../data-raw/storethat.sqlite", verbose = FALSE)

get_periods(equity_market)
#>            ticker       field      start        end
#>  1: ADM US Equity CUR_MKT_CAP 2016-10-31 2017-11-17
#>  2: ADM US Equity  EQY_SH_OUT 2016-10-31 2017-11-17
#>  3: ADM US Equity      PX_ASK 2016-10-31 2017-11-17
#>  4: ADM US Equity      PX_BID 2016-10-31 2017-11-17
#>  5: ADM US Equity     PX_HIGH 2016-10-31 2017-11-17
#>  6: ADM US Equity     PX_LAST 2016-10-31 2017-11-17
#>  7: ADM US Equity      PX_LOW 2016-10-31 2017-11-17
#>  8: ADM US Equity      PX_MID 2016-10-31 2017-11-17
#>  9: ADM US Equity     PX_OPEN 2016-10-31 2017-11-17
#> 10: ADM US Equity   PX_VOLUME 2016-10-31 2017-11-17
#> 11: KHC US Equity CUR_MKT_CAP 2016-10-31 2017-11-17
#> 12: KHC US Equity  EQY_SH_OUT 2016-10-31 2017-11-17
#> 13: KHC US Equity      PX_ASK 2016-10-31 2017-11-17
#> 14: KHC US Equity      PX_BID 2016-10-31 2017-11-17
#> 15: KHC US Equity     PX_HIGH 2016-10-31 2017-11-17
#> 16: KHC US Equity     PX_LAST 2016-10-31 2017-11-17
#> 17: KHC US Equity      PX_LOW 2016-10-31 2017-11-17
#> 18: KHC US Equity      PX_MID 2016-10-31 2017-11-17
#> 19: KHC US Equity     PX_OPEN 2016-10-31 2017-11-17
#> 20: KHC US Equity   PX_VOLUME 2016-10-31 2017-11-17
#> 21: XPO US Equity CUR_MKT_CAP 2016-10-31 2017-11-17
#> 22: XPO US Equity  EQY_SH_OUT 2016-10-31 2017-11-17
#> 23: XPO US Equity      PX_ASK 2016-10-31 2017-11-17
#> 24: XPO US Equity      PX_BID 2016-10-31 2017-11-17
#> 25: XPO US Equity     PX_HIGH 2016-10-31 2017-11-17
#> 26: XPO US Equity     PX_LAST 2016-10-31 2017-11-17
#> 27: XPO US Equity      PX_LOW 2016-10-31 2017-11-17
#> 28: XPO US Equity      PX_MID 2016-10-31 2017-11-17
#> 29: XPO US Equity     PX_OPEN 2016-10-31 2017-11-17
#> 30: XPO US Equity   PX_VOLUME 2016-10-31 2017-11-17
#>            ticker       field      start        end

storethat

All the objects above can be stored in a bespoke database for later off-Bloomberg consumption. The storethat package makes the process seamless by providing befitted storing methods:

library(storethat)

db_create()

db_store(object = futures_TS, file = "~/storethat.sqlite", verbose = FALSE)
db_store(object = fund_market, file = "~/storethat.sqlite", verbose = FALSE)

Every function above has an equivalent for retrieving data from a storethat database that can be accessed by swapping the ‘BBG’ prefix for ‘storethat’; i.e. ‘BBG_futures_market’ becomes ‘storethat_futures_market’. Function parameters are identical for both data sources and the returned objects have alike characteristics:

futures_TS <- storethat_futures_market(
  type = "term structure", active_contract_tickers = futures_tickers,start, end, 
  TS_positions = 1L:5L, roll_type = "A", roll_days = 0L,  
  roll_months = 0L, roll_adjustment = "N", verbose = FALSE)

fund_market <- storethat_fund_market(fund_tickers, start, end, verbose = FALSE)

Updating a storethat database is equally straightforward with pullit. Update the whole equity content of the database with:

storethat_update(instrument = "equity", verbose = FALSE)

More refined updates are also allowed:

storethat_update(instrument = "equity", book = "market")

pullit!

Olivier Bauthéac

2018-11-18

Bloomberg

equity

market

books

balance sheet

cash flow statement

income statement

key stats

ratios

info

fund

market

info

futures

market

term structure

aggregate

CFTC

info

accessors

storethat

plotit

futures term structure

fund market

references